Automatic Annotation of Sport Video Content
Identifieur interne : 002D03 ( Istex/Corpus ); précédent : 002D02; suivant : 002D04Automatic Annotation of Sport Video Content
Auteurs : Marco Bertini ; Alberto Del Bimbo ; Walter NunziatiSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Automatic semantic annotation of video streams allows to extract significant clips for archiving and retrieval of video content. In this paper, we present a system that performs automatic annotation of soccer videos, detecting principal highlights, and recognizing identity of players. Highlight detection is carried out by means of finite state machines that encode domain knowledge, while player identification is based on face detection, and on the analysis of contextual information such as jersey’s numbers and superimposed text captions. Results obtained on actual soccer videos shows overall highlight detection rates of about 90%. Lower, but still promising, accuracy is achieved on the very difficult player identification task.
Url:
DOI: 10.1007/11578079_109
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: Automatic semantic annotation of video streams allows to extract significant clips for archiving and retrieval of video content. In this paper, we present a system that performs automatic annotation of soccer videos, detecting principal highlights, and recognizing identity of players. Highlight detection is carried out by means of finite state machines that encode domain knowledge, while player identification is based on face detection, and on the analysis of contextual information such as jersey’s numbers and superimposed text captions. Results obtained on actual soccer videos shows overall highlight detection rates of about 90%. Lower, but still promising, accuracy is achieved on the very difficult player identification task.</div>
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<Para>Automatic semantic annotation of video streams allows to extract significant clips for archiving and retrieval of video content. In this paper, we present a system that performs automatic annotation of soccer videos, detecting principal highlights, and recognizing identity of players. Highlight detection is carried out by means of finite state machines that encode domain knowledge, while player identification is based on face detection, and on the analysis of contextual information such as jersey’s numbers and superimposed text captions. Results obtained on actual soccer videos shows overall highlight detection rates of about 90%. Lower, but still promising, accuracy is achieved on the very difficult player identification task.</Para>
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<abstract lang="en">Abstract: Automatic semantic annotation of video streams allows to extract significant clips for archiving and retrieval of video content. In this paper, we present a system that performs automatic annotation of soccer videos, detecting principal highlights, and recognizing identity of players. Highlight detection is carried out by means of finite state machines that encode domain knowledge, while player identification is based on face detection, and on the analysis of contextual information such as jersey’s numbers and superimposed text captions. Results obtained on actual soccer videos shows overall highlight detection rates of about 90%. Lower, but still promising, accuracy is achieved on the very difficult player identification task.</abstract>
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